Method for determining lens and apparatus using the method

ABSTRACT

A method for determining a lens and apparatus using the method are disclosed. According to an embodiment, a method for determining a lens to be inserted into an eyeball during lens implant surgery using machine learning may be provided, the method comprising: obtaining a plurality of examination data of a person to be operated on; and determining a size of a lens to be inserted into an eyeball of the person among a plurality of lens sizes by inputting the obtained plurality of examination data of the person to a lens determination model, wherein the lens determination model is different from a formula for determining a lens to be inserted into an eyeball during lens implant surgery, and is trained based on examination data of patients who have had lens implant surgery in the past and size information of lenses inserted into eyeballs of the patients.

TECHNICAL FIELD

The following embodiments relate to a method of determining a lens usedfor lens implant surgery and a device using the same, and moreparticularly, to a method and device for determining a lens used forlens implant surgery using artificial intelligence.

BACKGROUND ART

Intraocular lens implant surgery, which is one of surgery methods thatcorrect degraded uncorrected vision due to ametropia, is for inserting aspecial lens, which is designed to correct a refractive disorder, into anormal eyeball with a crystalline lens.

In the related art, during lens implant surgery, a lens is selectedusing a program provided by a lens manufacturer. In this case, onlybasic eyeball-related values of a person to be operated on are used asinput data, and the size and power of the lens are determined withoutconsidering characteristics of an eyeball of the person to be operatedon. In general, a program provided by a lens manufacturer is made basedon a simple formula, and in the formula, only basic eyeball-relatedvalues of a person to be operated on are used as input data. As aresult, since many side effects such as cataract and glaucoma are causeddue to the insertion of a lens with an inappropriate size and power, theperson to be operated on should undergo revision surgery such as surgeryfor removing a lens.

Recently, various research on lens implant surgery has been conducted toprevent side effects and improve the quality of vision.

DISCLOSURE Technical Problem

One object relates to provide information about an implantable lenssuitable for characteristics of an eyeball of a person to be operated onwith lens implant surgery by using machine learning.

One object relates to provide a lens determination assistance systemusing artificial intelligence and a lens determination assistanceprocess using artificial intelligence.

One object relates to determine a lens more suitable for a person to beoperated on with lens implant surgery in consideration ofcharacteristics of an eyeball of each person to be operated on, reducethe probability of occurrence of side effects of the lens implantsurgery, and improve quality of vision.

Objects may not be limited to the above, and other objects will beclearly understandable to those having ordinary skill in the art fromthe disclosures provided below together with accompanying drawings.

Technical Solution

According to an embodiment, it is possible to provide a method ofdetermining a lens to be inserted into an eyeball of a person to beoperated on during lens implant surgery through a lens determinationmodel trained using machining learning.

Technical solutions may not be limited to the above, and other technicalsolutions will be clearly understandable to those having ordinary skillin the art from the disclosures provided below together withaccompanying drawings.

Advantageous Effects

According to an embodiment, by determining a lens having a lens size anda lens power suitable for an eyeball of a person to be operated on withlens implant surgery, it is possible to minimize occurrence of sideeffects after surgery.

According to an embodiment, by determining a lens having a lens size anda lens power suitable for an eyeball of a person to be operated on withlens implant surgery, it is possible to prevent revision surgery fromneeding to be performed for lens implant surgery.

Advantageous effects may not be limited to the above, and other effectswill be clearly understandable to those having ordinary skill in the artfrom the disclosures provided below together with accompanying drawings.

DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram for describing a position at which a lens isinserted during lens implant surgery.

FIG. 2 is a diagram illustrating a lens determination assistance systemaccording to an embodiment.

FIG. 3 is a diagram illustrating a lens determination assistance systemaccording to another embodiment.

FIG. 4 is a diagram illustrating a lens determination assistance systemaccording to another embodiment.

FIG. 5 is a diagram illustrating a lens determination assistance systemusing a server.

FIG. 6 is a diagram illustrating a relationship between a server deviceand a client device.

FIG. 7 is a diagram illustrating a lens determination model according toan embodiment.

FIG. 8 is a diagram of a lens determination assistance process accordingto an embodiment.

FIG. 9 is a diagram of a lens size determination module according to anembodiment.

FIG. 10 shows diagrams for describing side effects of lens implantsurgery according to an embodiment.

FIG. 11 is a diagram of a lens size determination process according toan embodiment.

FIG. 12 is a diagram illustrating the determination of a lens sizeaccording to an embodiment.

FIG. 13 is a diagram illustrating the determination of the lens sizeaccording to an embodiment.

FIG. 14 is a diagram illustrating the determination of a lens sizeaccording to another embodiment.

FIG. 15 is a diagram illustrating the determination of the lens sizeaccording to another embodiment.

FIG. 16 is a diagram illustrating the determination of a lens sizeaccording to still another embodiment.

FIG. 17 is a schematic diagram illustrating a plurality of sub-models ofa lens size determination model.

FIG. 18 is a diagram for defining a vaulting value.

FIG. 19 is a diagram illustrating a vaulting value prediction moduleaccording to an embodiment.

FIG. 20 is a diagram of a vaulting value prediction process according toan embodiment.

FIG. 21 is a diagram illustrating the prediction of a vaulting valueaccording to an embodiment.

FIG. 22 is a diagram illustrating the prediction of a vaulting valueaccording to another embodiment.

FIG. 23 is a diagram of a lens power determination module according toan embodiment.

FIG. 24 is a diagram of a lens power determination process according toan embodiment.

FIG. 25 shows an example that occurs in a corneal incision process of aperson to be operated on.

FIG. 26 is a diagram illustrating the determination of a lens poweraccording to an embodiment.

FIG. 27 is a diagram illustrating the determination of a lens poweraccording to another embodiment.

BEST MODE

According to an embodiment, a method for determining a lens to beinserted into an eyeball during lens implant surgery using machinelearning may comprise obtaining a plurality of examination data of aperson to be operated on; and determining a size of a lens to beinserted into an eyeball of the person among a plurality of lens sizesby inputting the obtained plurality of examination data of the person toa lens determination model, wherein the lens determination model may bedifferent from a formula for determining a lens to be inserted into aneyeball during lens implant surgery, and may be trained based onexamination data of patients who have had lens implant surgery in thepast and size information of lenses inserted into eyeballs of thepatients.

MODE FOR INVENTION

According to an embodiment, a method for determining a lens to beinserted into an eyeball during lens implant surgery using machinelearning may comprise obtaining a plurality of examination data of aperson to be operated on; and determining a size of a lens to beinserted into an eyeball of the person among a plurality of lens sizesby inputting the obtained plurality of examination data of the person toa lens determination model, wherein the lens determination model may bedifferent from a formula for determining a lens to be inserted into aneyeball during lens implant surgery, and may be trained based onexamination data of patients who have had lens implant surgery in thepast and size information of lenses inserted into eyeballs of thepatients.

The plurality of examination data of the person may include one of afirst data and a second data, and a priority of the first data may behigher than the priority of the second data, wherein the priority may bea priority of an input data to increase an accuracy of a lens sizedetermined by inputting the input data to the lens determination model.

The determining the size of the lens may include when the plurality ofexamination data of the person does not include the first data andinclude the second data, determining the size of the lens to be insertedinto the eyeball of the person by inputting the second data to the lensdetermination model, and an accuracy of a lens size determined when theplurality of examination data of the person includes the first data maybe higher than an accuracy of a lens size determined when the pluralityof examination data of the person includes the second data instead ofthe first data.

The determining the size of the lens may include calculating areliability of an accuracy of a lens size derived according to theplurality of examination data of the person, and determining the size ofthe lens by providing the calculated reliability to a user.

According to an embodiment, the method may further comprise when theplurality of examination data of the person includes the second data orother data except the first data, estimating the first data from thesecond data or the other data, and in the estimating, an accuracycorresponding to a lens size derived when the first data is inputted tothe lens determination model may be higher than an accuracycorresponding to a lens size derived when the second data is inputted tothe lens determination model.

The first data among the plurality of examination data of the person mayinclude ATA (Anterior Chamber Angle), ACD-epi (Anterior Chamber Depth),ACD-endo, CCT (Central Corneal Thickness), CLR (crystalline lens rise),WTW, Axial Length, BUT, a distance between iris, and a space size valuefor a lens, and the first data is obtained by using a laser and/or ahigh-frequency ultrasound, and the second data may be obtained by usinga general ophthalmic examination.

In the determining the size of the lens, a size of a lens to be insertedinto an eyeball of the person may be determined as any one of aplurality of predetermined lens sizes.

In the determining the size of the lens, a size of a lens may bedetermined as any one of non-standardized lens sizes derived byinputting the plurality of examination data to the lens determinationmodel, not a plurality of predetermined lens sizes.

According to an embodiment, the method may further comprise determininga lens power to be inserted into an eyeball of the person among aplurality of lens powers by inputting the obtained plurality ofexamination data of the person to the lens determination model, whereinin the determining the lens power, the lens power may be determined sothat a target vision of the person is derived when a lens determined bythe lens determination model is inserted into the eyeball of the person,and wherein the lens determination model may be trained based onexamination data of patients who have had lens implant surgery in thepast and incision information of the patients.

The obtained plurality of examination data of the person may include adiopter, an astigmatism axis and an astigmatism direction parametermeasured from the eyeball of the person, and the determining the lenspower may include determining the lens power suitable for the targetvision of the person by inputting the plurality of examination data ofthe person and incision information expected during a corneal incisionprocess of the person's lens implant surgery to the lens determinationmodel.

When the plurality of examination data of the person is inputted to thelens determination model, a lens power to derive the target vision ofthe person and incision information expected during a corneal incisionprocess of the person's lens implant surgery may be determined.

The incision information may include at least one selected from thegroup of a corneal incision method, a corneal incision location, acorneal incision direction and/or a corneal incision degree, a locationof coma, a corneal astigmatism, a lenticular astigmatism, a ratio ofmyopia and astigmatism during a corneal incision process of the lensimplant surgery.

According to an embodiment, a device for determining a lens to beinserted into an eyeball during lens implant surgery using machinelearning, the device may comprising: a memory for storing a plurality ofexamination data of a person to be operated on; and a processor, whereinthe processor may be configured to obtain the stored plurality ofexamination data of the person from the memory, and determine a size ofa lens to be inserted into an eyeball of the person among a plurality oflens sizes by inputting the obtained plurality of examination data ofthe person to a lens determination model, and wherein the lensdetermination model may be different from a formula for determining alens to be inserted into an eyeball during lens implant surgery, and maybe trained based on examination data of patients who have had lensimplant surgery in the past and size information of lenses inserted intoeyeballs of the patients.

According to an embodiment, a method for predicting a vaulting valuerepresenting a distance between a rear surface of a lens to be insertedinto an eyeball of a person to be operated on with lens implant surgeryand an anterior surface of a crystalline lens may comprise: inputting aplurality of examination data of the person to be operated on and one ormore lens sizes to a vaulting value prediction model; and predicting thevaulting value corresponding to each of the one or more input lens sizesfrom the vaulting value prediction model, wherein the vaulting valueprediction model may be trained based on a plurality of examination dataof patients who have had lens implant surgery in the past, sizeinformation of lenses inserted into eyeballs of the patients, andvaulting values measured after surgery of the patients.

The vaulting value may be defined as the shortest distance among aplurality of distances between the rear surface of the lens to beinserted into the eyeball of the person to be operated on with the lensimplant surgery and the anterior surface of the crystalline lens.

The predicting of the vaulting value may include providing informationabout whether the input lens size is suitable for the lens to beinserted into the eyeball of the person to be operated on according towhether the predicted vaulting value satisfies a condition of apredetermined range.

When the predicted vaulting value satisfies the condition of thepredetermined range, information may be provided that the input lenssize is suitable for the lens to be inserted into the eyeball of theperson to be operated on, and when the predicted vaulting value does notsatisfy the condition of the predetermined range, information may beprovided that the input lens size is not suitable for the lens to beinserted into the eyeball of the person to be operated on.

The condition of the predetermined range may satisfy the predictedvaulting value being included within a range of 250 to 750 μm.

According to an embodiment, a method for predicting a vaulting valuerepresenting a distance between a rear surface of a lens to be insertedinto an eyeball of a person to be operated on with lens implant surgeryand an anterior surface of a crystalline lens may comprise: inputting aplurality of examination data of the person to be operated on to avaulting value prediction model; and predicting expected lens sizessuitable for the eyeball of the person to be operated on and thevaulting value corresponding to each of the expected lens sizes from thevaulting value prediction model, wherein the vaulting value predictionmodel may be trained based on a plurality of examination data ofpatients who have had lens implant surgery in the past, sizes of lensesinserted into eyeballs of the patients, and vaulting values measuredafter surgery of the patients.

The expected lens size suitable for the eyeball of the person to beoperated on may include any one selected from a plurality of preset lenssizes.

The expected lens size suitable for the eyeball of the person to beoperated on may include any one selected from non-standardized lenssizes rather than the plurality of preset lens sizes.

According to an embodiment, a device for predicting a vaulting valuerepresenting a distance between a rear surface of a lens to be insertedinto an eyeball of a person to be operated on with lens implant surgeryand an anterior surface of a crystalline lens may comprise: a memory,which stores a plurality of examination data of the person to beoperated on; and a processor, wherein the processor may input theplurality of examination data of the person to be operated on and one ormore lens sizes to a vaulting value prediction model and may predict thevaulting value corresponding to each of the one or more input lens sizesfrom the vaulting value prediction model, and the vaulting valueprediction model may be trained based on a plurality of examination dataof patients who have had lens implant surgery in the past, sizes oflenses inserted into eyeballs of the patients, and vaulting valuesmeasured after surgery of the patients.

Hereinafter, specific embodiments of the present invention will bedescribed in detail with reference to the accompanying drawings.Meanwhile, the spirit of the present invention is not limited to thesuggested embodiments, and those skilled in the art to which the presentinvention pertains could easily suggest a more retrogressive inventionor another embodiment which falls within the spirit of the presentinvention through the addition, modification, and deletion of anothercomponent without departing from the spirit of the present invention.

The same reference numerals will be used throughout to designate thesame or like components having the same function within the same scopeshown in the drawings of the embodiments.

1. Definition of Terms

(1) Lens Implant Surgery

Lens implant surgery is one of surgery methods that correct degradeduncorrected vision due to ametropia and is a surgery for inserting aspecial lens, which is designed to correct a refractive disorder, into anormal eyeball with a crystalline lens.

FIG. 1 is a diagram for describing a position at which a lens isinserted during lens implant surgery. As types of lens implant surgery,there are anterior chamber lens implant surgery in which a lens isinserted between a cornea Co and an iris I and posterior chamber lensimplant surgery in which a lens is inserted into a space between a backof an iris and a crystalline lens. Referring to FIG. 1 , in theposterior chamber lens implant surgery, a lens may be inserted at aposition IN1 (first lens insertion portion), and in the anterior chamberlens implant surgery, a lens may be inserted at a position IN2 (secondlens insertion portion). Hereinafter, for convenience of description,the present invention will be described based on the posterior chamberlens implant surgery that is also referred to as implantable collamerlens (ICL) implant surgery. However, the present invention is notlimited thereto, and of course, the present invention may be applied tothe anterior camber lens implant surgery.

(2) Lens

In the present specification, a lens may refer to an intraocular lensused in lens implant surgery and may be distinguished from a hard lensand a soft lens worn on an eyeball surface.

Information about the lens may include information about a lens size andinformation about a lens power. The lens size may include a plurality oflens sizes. The lens power may include a plurality of lens powers. Inaddition, the expression “lens determination” may refer to thedetermination of any one of combinations of a plurality of lens sizesand a plurality of lens powers.

(3) Lens Determination Model

A lens determination model refers to an algorithm and/or model thatdetermine an intraocular lens inserted into an eyeball using artificialintelligence. When examination data of a person to be operated on isinput as input data through the model, a lens determination modeldescribed below is a model for deriving information about a lens to beinserted into an eyeball of the person to be operated on as output datacorresponding to the input data. Hereinafter, a configuration,generation process, and determination process of a lens determinationmodel will be described in detail.

(4) Learning

Learning refers to a process in which a lens determination model istrained based on learning data and labeling data or unlabeled data todetermine output data with respect to input data. That is, the lensdetermination model forms a rule to determine the data.

The lens determination model may be trained through learning data.Training the lens determination model means adjusting a weight of themodel.

As learning methods, there are various methods including supervisedlearning, unsupervised learning, reinforcement learning, and imitationlearning.

2. Lens Determination Assistance System 2.1. Configuration of LensDetermination Assistance System

FIG. 2 illustrates a lens determination assistance system 1 according toan embodiment. Referring to FIG. 2 , the lens determination assistancesystem 1 may include a lens size determination module 1000 which derivesa lens size of information about a lens to be inserted into an eyeballof a person to be operated on, a vaulting value prediction module 2000which assists in determining the lens size, and a lens powerdetermination module 3000 which derives a lens power.

The lens determination assistance system 1 may perform functions ofdetermining a lens size, predicting a vaulting value, and determining alens power. Specifically, the lens determination assistance system 1 maydetermine a lens size, predict a vaulting value, and determine a lenspower using a lens determination model trained through machine learning.

Of course, in FIG. 2 , the lens determination assistance system 1 isillustrated as including all of the lens size determination module 1000,the vaulting value prediction module 2000, and the lens powerdetermination module 3000, but not limited thereto. In some cases, thelens determination assistance system may include at least one selectedfrom the group of the lens size determination module, the vaulting valueprediction module, and the lens power determination module.

In addition, the lens size determination module 1000, the vaulting valueprediction module 2000, and the lens power determination module 3000 maybe implemented in one device or different devices. For example, when thelens size determination module configured to derive a lens size in thelens determination assistance system 1 is implemented in any one device,only size information of the lens to be inserted into the eyeball of theperson to be operated on may be acquired.

Alternatively, in the lens determination assistance system 1, at leasttwo modules of the lens size determination module, the vaulting valueprediction module, and the lens power determination module may beimplemented to interwork with each other. For example, in order toacquire size information of a lens to be inserted into an eyeball of aperson to be operated on, the lens size determination module and thevaulting value prediction module may be combined and may be implementedto interwork with each other, thereby deriving a vaulting value that ispredicted together with a size of a lens to be inserted. Hereinafter,each module of the lens determination assistance system will bedescribed one by one.

FIG. 3 is a diagram illustrating a configuration of a training device 11and a determination assistance device 21 of the lens determinationassistance system 1. In an embodiment, the lens determination assistancesystem 1 may include the training device 11 and the determinationassistance device 21.

The training device 11 may train the lens determination model.Specifically, the training device 11 may train the lens determinationmodel based on learning data. The training device 11 may train the lensdetermination model through various learning methods. For example, thetraining device 11 may train the lens determination model throughmethods including supervised learning, unsupervised learning,reinforcement learning, and imitation learning. The training device 11may train the lens determination model by providing labeled data for thelearning data. However, the labeled data is not necessarily used, andunlabeled data may be used.

The determination assistance device 21 may receive the trained lensdetermination model from the training device 11 to use the trained lensdetermination model. Specifically, the determination assistance device21 may output auxiliary information for determining a lens to beinserted into an eyeball of a person to be operated on using the trainedlens determination model. Specifically, when receiving input data suchas examination data of the person to be operated on, the determinationassistance device 21 may output information about a lens suitable for aneyeball of the person to be operated on. The determination assistancedevice 21 may enable a user to determine a lens to be inserted into theeyeball of the person to be operated on with lens implant surgerythrough the output information about the lens.

The information about the lens may be information about a lens size, apredicted vaulting value, and a lens power.

The lens determination model trained using the learning data in thetraining device 11 may be transmitted to the determination assistancedevice 21. Of course, in FIG. 3 , the training device 11 and thedetermination assistance device 21 are illustrated as being separated,but not limited thereto. In some cases, the training device 11 and thedetermination assistance device 21 may be implemented to be separatedand may be implemented as one without being separated. As an example,the determination assistance device may be the same device as thetraining device or may be a device separate from the training device.

FIG. 4 is a diagram illustrating a configuration of the training deviceand/or the determination assistance device. Referring to FIG. 4 , thetraining device and/or the determination assistance device may include amemory unit 31, a control unit 33, and a communication unit 35.

The training device and/or the determination assistance device mayinclude the control unit 33. The control unit 33 may control operationsof the training device and/or the determination assistance device. Thecontrol unit 33 may read a system program and various processingprograms stored in the memory unit 31.

The control unit 33 may include one or more of a central processing unit(CPU), a random access memory (RAM), a graphic processing unit (GPU),one or more microprocessors, and other electronic components capable ofprocessing input data according to preset logic.

The training device and/or the determination assistance device mayinclude the memory unit 31. The memory unit 31 may store data and alearning model which are required for learning. The memory unit 31 maystore examination data of a person to be operated on.

The memory unit 31 may store learning data, labeling data, unlabeleddata, input data, output data, and the like.

The memory unit 31 may be implemented using a nonvolatile semiconductormemory, a hard disk, a flash memory, a RAM, a read-only memory (ROM), anelectrically erasable programmable ROM (EEPROM), or other tangiblenonvolatile recording media.

The memory unit 31 may store various processing programs, parameters forprocessing programs, result data of such processing, or the like.

The training device and/or the determination assistance device mayfurther include the communication unit 35. The communication unit 35 maycommunicate with an external device. The communication unit 35 mayperform wired or wireless communication. The communication unit 35 mayperform bidirectional or unidirectional communication.

The training device and/or the determination assistance device mayinclude a processor, a volatile memory, a nonvolatile memory, a massstorage device, and a communication interface. The processor may performtraining on the lens determination model through the training deviceand/or the determination assistance device.

FIG. 5 is a schematic diagram illustrating a lens determinationassistance system using a server. Referring to FIG. 5 , the lensdetermination assistance system may include a plurality of clientdevices and a server device. Hereinafter, a first client device amongthe plurality of client devices is exemplarily described, but a secondclient device may perform the same operation.

A first client device 50-1 may request information from a server device40 and acquire lens determination auxiliary information transmitted inresponse to the request and may request lens determination auxiliaryinformation from the server device 40.

The first client device 50-1 may acquire data necessary for lensdetermination and may transmit data acquired from the determinationassistance device.

The first client device 50-1 may be a portable device such as asmartphone or a tablet personal computer (PC).

The server device 40 may store and/or drive a lens determination model.The server device 40 may store weights constituting the trained lensdetermination model. The server device 40 may collect and/or store dataused to assist in lens determination.

The server device 40 may output results of a lens determinationassistance process using the lens determination model to the firstclient device 50-1. The server device 40 may acquire feedback from thefirst client device 50-1.

In an embodiment, the first client device 50-1 may acquire the lensdetermination model from the server device 40 and drive the acquiredlens determination model. In this case, the first client device 50-1 mayacquire lens determination auxiliary information by driving the lensdetermination model without providing input data to the server device40.

The server device 40 may communicate with the first client device 50-1configured to acquire first lens determination auxiliary informationand/or a second client device 50-2 configured to acquire second lensdetermination auxiliary information.

FIG. 6 is a schematic diagram illustrating a relationship between theserver device 40 and a client device 50. Referring to FIG. 6 , theserver device 40 may communicate with the client device 50 through acommunication unit. The communication unit may perform wired or wirelesscommunication. The communication unit may perform bidirectional orunidirectional communication. The client device 50 may also communicatewith the server device 40 through a communication unit.

In an embodiment, when the client device 50 transmits input data of aperson to be operated on to the server device, the server device 40 mayreceive information about a lens to be inserted into an eyeball of theperson to be operated on using a trained lens determination model.

In an embodiment, a control unit 53 of the client device 50 may acquireinput data from a memory unit 51, and the acquired input data may betransmitted to a communication unit 45 of the server device 40 through acommunication unit 55. In addition, a control unit 43 of the serverdevice 40 obtains a result value by inputting input data to a lensdetermination model stored in a memory unit 41, and the obtained resultvalue may be transmitted to the communication unit 55 of the clientdevice 50 using the communication unit 45.

2.2. Lens Determination Model

FIG. 7 is a diagram illustrating a lens determination model. Referringto FIG. 7 , a lens determination model 100 may include a lens sizedetermination model 110, a vaulting value prediction model 120, and alens power determination model 130.

Of course, in FIG. 7 , the lens determination model 100 is illustratedas including all of the lens size determination model 110, the vaultingvalue prediction model 120, and the lens power determination model 130,but not limited thereto. In some cases, the lens determination model 100may include at least one selected from the group of the lens sizedetermination model, the vaulting value prediction model, and the lenspower determination model.

In an embodiment, the lens determination model 100 may include the lenssize determination model and the lens power determination model or mayinclude the lens size determination model and the vaulting valueprediction model.

In addition, the lens determination model 100 may be implemented in onedevice or different devices. For example, when the lens determinationmodel includes the lens size determination model and the vaulting valueprediction model, the models may be implemented in one device tointerwork with each other. Alternatively, when the lens determinationmodel 100 includes the lens size determination model and the lens powerdetermination model, the lens size determination model may beimplemented in a different device independently from the lens powerdetermination model.

FIG. 8 is a diagram of a lens determination assistance process.Referring to FIG. 8 , the lens determination assistance process may beconsidered by being mainly divided into a training operation S100 oftraining a lens determination model and a determining operation S200 ofperforming a lens determination model using the trained lensdetermination model.

Referring to FIG. 8 , the training operation S100 may be a process oftraining the lens determination model using learning data. In addition,the training operation S100 may be performed by the training device.

According to an embodiment, in the training operation S100, the learningdata may be acquired, and the lens determination model may be trainedusing the acquired data. That is, the training operation S100 is aprocess of generating the lens determination model, and model parametersconstituting the lens determination model may be obtained according tothe generation of the lens determination model. As an example, the modelparameters may include weights adjusted when the lens determinationmodel is trained.

In an embodiment, the learning data may include a plurality ofexamination data of patients who have had lens implant surgery in thepast, information about lenses inserted into eyeballs of the patients(lens size and lens power), surgery parameters, and vaulting value datameasured after surgery of the patients.

In addition, the examination data of the learning data may includeexamination data acquired from a plurality of examination apparatusesrelated to measurement of an eyeball of the patients who have had thelens implant surgery in the past.

In an embodiment, the information about the lens of the learning datamay include a lens size and/or a lens power of the lens inserted intothe eyeball of the patient who has had the lens implant surgery in thepast. In this case, when side effects have not occurred in the patientwho has had the lens implant surgery in the past after the lens implantsurgery, the information about the lens may include a lens size and/or alens power of the lens inserted into the eyeball of the patient. Ofcourse, according to some embodiments, when side effects have occurredin the patient who has had the lens implant surgery in the past afterthe lens implant surgery, the information about the lens may include alens size and/or a lens power of the lens inserted into the eyeball ofthe patient.

In addition, the surgical parameters of the learning data may be relatedto corneal incision information during a corneal incision process of thepatient who has had the lens implant surgery in the past. As an example,the surgical parameters may include a corneal incision method, a cornealincision location, a corneal incision degree, and the like.

In an embodiment, the vaulting value data of the learning data may referto a value representing a distance between a rear surface of a lens tobe inserted into an eyeball of a person to be operated on with lensimplant surgery and an anterior surface of a crystalline lens and mayrefer to vaulting value data measured on the patient who has had thelens implant surgery in the past.

The lens determination model may be a model that outputs informationabout a lens based on the learning data. At least one of a plurality oflearning algorithms for calculating information about a lens may beselected as the lens determination model. For example, the algorithm maybe a logistic regression, a K-nearest neighbor algorithm, a supportvector machine, a decision tree, or the like.

The lens determination model may use multiple learning algorithms amonga plurality of learning algorithms for calculating a predicted value.For example, an ensemble method may be used in the lens determinationmodel, and better prediction performance may be obtained as comparedwith when learning algorithms are separately used.

The lens determination model may be implemented in the form of aclassifier that generates information about a lens. The classifier mayperform double-classification or multi-classification.

The lens determination model may be implemented in the form of aregression so as to derive information of a lens. A regression methodmay be a linear regression method, a logistic regression method, or thelike.

In an embodiment, the training operation S100 may be performed byobtaining a result value (output data) using a model to which arbitraryweights are given, comparing the obtained result value (output data)with labeling data of learning data, and performing backpropagationaccording to an error to optimize the weights.

Although not shown, the training operation S100 may include anevaluating operation of evaluating performance of the trained lensdetermination model. In the evaluating operation, the lens determinationmodel may be evaluated using an evaluation data set. The evaluatingoperation of the lens determination model may be an operation ofevaluating the lens determination model trained in the trainingoperation and predicting new data using the lens determination model.Specifically, the evaluation operation may be an operation of measuringwhether the trained lens determination model can be generalized to thenew data.

In addition, in the training operation S100 of the lens determinationmodel, a learning data set and an evaluation data set may bedistinguished. Here, the learning data set may refer to a set oflearning data used in a training process of the training operation, andthe evaluation data set may refer to a set of evaluation data used in anevaluating process of the evaluating operation. In this case, thelearning data set used to train the lens determination model may not beused in the evaluating operation of the lens determination model.

In addition, referring to FIG. 8 , the determining operation S200 mayuse the trained lens determination model trained by obtaining the modelparameters in the training operation. Specifically, in the determiningoperation S200, after input data, such as examination data of the personto be operated on, is acquired, information (result value) about a lensto be inserted into an eyeball of the person to be operated on may beacquired using the trained lens determination model. In addition, thedetermining operation S200 may be performed by the determinationassistance device.

The input data may include a plurality of examination data of the personto be operated on with the lens implant surgery.

The result value may include the information about the lens to beinserted into the eyeball of the person to be operated on. Theinformation about the lens may include a lens size, a lens power, apredicted vaulting value, and the like. Hereinafter, the determinationof the lens size, the prediction of the vaulting value, and thedetermination of the lens power will be described in more detail.

3. Determination of Lens Size 3.1. Configuration of Lens SizeDetermination Module

FIG. 9 is a diagram illustrating a configuration of the lens sizedetermination module 1000. In an embodiment, the lens size determinationmodule 1000 may output a lens size of a lens to be inserted into aneyeball of a person to be operated on from input data.

Referring to FIG. 9 , the lens size determination module 1000 mayinclude an input unit 1100, a lens size determination unit 1300, and anoutput unit 1500.

The input unit 1100 may acquire input data from a database. Here, theinput data may be a plurality of examination data of the person to beoperated on.

Specifically, the input unit 1100 may be connected directly to thedatabase to acquire the input data. In addition, the input unit 1100 mayreceive and acquire the input data from a server or other externaldevices.

The input data may be the examination data of the person to be operatedon. The input data may include a plurality of parameters. Specifically,the input data may include examination data representing differentparameters obtained from the same or different examination apparatusesor may include examination data representing the same parametersobtained from different examination apparatuses.

In addition, the input data may include examination data measured at thesame time point or may include examination data measured at differenttime points.

The input data may be the same and/or different parameters obtained fromthe same examination apparatus or may be the same and/or differentparameters obtained from different examination apparatuses.

In addition, the input data may be provided with one piece of input dataor a plurality of pieces of input data. The pieces of input data mayhave different degrees of influence on a result value (lens size of thelens to be inserted into the eyeball of the person to be operated on).In an embodiment, each pieces of input data may include parameters, anda degree to which the input data has an influence on a result value mayvary according to types of the parameters included in the pieces ofinput data. Here, the parameters may be defined to representcharacteristics of the eyeball of the person to be operated on and maybe expressed numerically. For example, the parameters may include anangle-to-angle (ATA) distance indicating a distance between anteriorangles, an anterior chamber depth (ACD)-epi, an ACD-endo, a centralcorneal thickness (CCT), a crystalline lens rise (CLR), a white-to-white(WTW), an axial length (AL), corneal curvature, a refractive error(myopia, astigmatism, farsightedness degree), a pupil size, anintraocular pressure, vision, a corneal shape, a corneal thickness, aneyeball length, a lens insertion space, and the like.

The person to be operated on may include a person who is to undergosurgery by selecting lens implant surgery among vision correctionsurgery. The person to be operated on may be a person who is to undergolens implant surgery using information about a lens output from the lensdetermination assistance system. A user may be a person who acquiresinformation on a lens to be inserted into an eyeball of the person to beoperated on using the lens determination assistance system. For example,the user may include a doctor who performs lens implant surgery, a lensmanufacturer, or the like.

The plurality of examination data may be data acquired from a pluralityof examination apparatuses which measure an eyeball and may include aplurality of eyeball-related parameters.

In addition, the examination data may include medical inquiry data(interview data). Specifically, the examination data may include targetvision and the like desired after the lens implant surgery of the personto be operated on.

Furthermore, the examination data may be measurement data about acornea. For example, the examination data may include a corneal shape,corneal symmetry, corneal thickness measurement data, corneal structuretomography data, corneal shape analysis data, corneal curvature, cornealendotheliocyte examination data, and the like.

In addition, the examination data may be measurement data about visionand/or refraction. For example, the examination data may include powerdata of glasses worn in the past, optometry, refractive errors (myopia,astigmatism, and farsightedness degrees), and the like.

In addition, the examination data may be measurement data of a distancein an eyeball. Specifically, the examination data may include a pupilsize, an eyeball length, and a distance of a space in which a lens is tobe inserted.

In addition, the examination data may be data about eye diseases and/orchronic diseases. For example, the examination data may include dataabout the presence or absence of retinal diseases, glaucoma, retinaldegeneration, or the like, cataract, diseases of a posterior surface ofan iris, and the like.

In addition, the examination data may be measurement data about aretina. For example, the examination data may include an image capturedby photographing a fundus retina or the like.

The examination data may be measured using one or more apparatuses.

Of course, the plurality of examination data may not be limited to onlydata acquired from a plurality of apparatuses for measuring an eyeball.In addition to the acquired data, the plurality of examination data mayinclude a variety of data. For example, the examination data may includeexamination data about eye-related genes, blood, or the like.

When input data, such as the plurality of examination data of the personto be operated on, is inputted, the lens size determination unit 1300may determine a lens size suitable for the eyeball of the person to beoperated on. Here, the suitable lens size may mean a lens size in whichthe possibility of occurrence of side effects is minimized when lensimplant surgery is performed on the person to be operated on. Specificoperations of the lens size determination unit 1300 will be described inmore detail with reference to FIG. 11 .

In an embodiment, the lens size determination unit 1300 may calculatereliability of accuracy of a lens size derived according to theexamination data of the person to be operated on. Information about thereliability may be pre-stored or may be provided from the outside. Forexample, information including that, among the plurality of examinationapparatuses, a result value of a first apparatus has a reliability of90% and a result value of a second apparatus has a reliability of 80%may be received through an external device that stores the informationabout the reliability in advance. In an embodiment, the lens sizedetermination model 110 may calculate reliability of accuracy of thelens size derived according to the examination data of the person to beoperated on based on reliability pre-stored in a training operation. Forexample, in a case of using examination data measured using the firstapparatus, reliability of accuracy of an output lens size may becalculated as 90%, and the calculation result may be presented to a userthrough the output unit 1500.

The output unit 1500 may output a lens size obtained through the lenssize determination unit 1300 to the user. In an embodiment, the outputunit 1500 may provide a display that visually outputs output data on ascreen. In addition, the output unit 1500 may output various forms suchas an image and a text.

The output unit 1500 may output the information (output data) about thelens size of the lens to be inserted into the eyeball of the person tobe operated on through the lens size determination unit 1300.

The output unit 1500 may output a standardized lens size according to alearning method of the lens size determination model.

According to an embodiment, when the lens size determination model isimplemented in the form of a classifier, the output unit may output astandardized lens size. The standardized lens size may be an existinglens size. The existing lens size may be a size that is predeterminedaccording to a preset standard. For example, the standardized lens sizemay be 12.1 mm, 12.6 mm, 13.2 mm, 13.7 mm. More detailed descriptionsthereof will be given in Content 3.3 below. However, it is not limitedthereto, and when the lens size determination model is implemented inthe form of a regression, the output unit may output a non-standardizedlens size. Unlike the standardized lens size, the non-standardized lenssize may not refer to any one selected from a predetermined category butmay refer to a numerical value of a lens size. The output unit mayoutput the numerical value of the lens size. This will be described indetail in Content 3.3.

FIG. 10 shows diagrams for describing side effects of lens implantsurgery. Referring to FIG. 10 , left and right of FIG. 10 illustratethat a lens having an unsuitable lens size is inserted after lensimplant surgery, and center of FIG. 10 illustrates that a lens having asuitable lens size is inserted during lens implant surgery. In addition,I represents an iris, La, Lb, and Lc represent a lens inserted into aneyeball, and C represents a crystalline lens.

The side effects of the lens implant surgery may occur when surgery isperformed using a lens having an unsuitable lens size withoutconsidering characteristics of an eyeball of a person to be operated on.For example, referring to left of FIG. 10 , the lens La having a certainlens size may be inserted into an eyeball of a person to be operated on.In this case, a lens having a small lens size is selected and insertedwithout considering characteristics of the eyeball of the person to beoperated on, and thus, friction is generated between the lens and thecrystalline lens to cause damage to the crystalline lens, therebycausing a cataract. In addition, referring to right of FIG. 10 , thelens Lc having a certain lens size may be inserted into an eyeball of aperson to be operated on. In this case, a lens having a large lens sizeis selected and inserted without considering characteristics of theeyeball of the person to be operated on, and thus, an end portion of thelens LC having a certain lens size is sandwiched between the crystallinelens and the iris to block a flow of a hydatoid, thereby causingglaucoma.

Therefore, in the lens implant surgery, a lens size, in which thepossibility of occurrence of side effects is minimized, should bedetermined in consideration of characteristics of an eyeball of a personto be operated on. As an embodiment, referring to center of FIG. 10 ,the lens Lb having a certain lens size may be determined inconsideration of characteristics of an eyeball of a person who is to beoperated on and may have a lens size suitable for the eyeball of theperson to be operated on. The lens Lb may have a size that does notcause friction between the lens Lb and the crystalline lens C, may beinserted at a position at which an appropriate distance between the lensLB and the iris is maintained, and may have a lens size by which thepossibility of occurrence of side effects is minimized.

As described above, in consideration of characteristics of an eyeball ofa person to be operated, only when a lens having a lens size inconsideration of sizes of a lens inserted into the eyeball, acrystalline lens, an iris, and a lens insertion space is inserted, canthe possibility of occurrence of side effects be minimized.

3.2. Lens Size Determination Process

FIG. 11 is a flowchart illustrating a lens size determination processS1000. Referring to FIG. 11 , the lens size determination process S1000may include acquiring input data such as a plurality of examination dataof a person to be operated on (S1100) and deriving a lens size using alens size determination model (S1300). The lens size determinationprocess S1000 may be performed by the lens size determination module1000 described above with reference to FIG. 2 .

Specifically, in operation S1100 of acquiring the input data, the inputdata may include a plurality of examination data acquired from aplurality of examination apparatuses related to measurement of aneyeball of the person to be operated on. In an embodiment, the pluralityof examination apparatuses related to the measurement of the eyeball maybe examination apparatuses that perform measurement using a laser and/orhigh-frequency ultrasound. For example, the examination apparatuses mayinclude an ultrasound biomicroscopy (UBM) apparatus, an opticalcoherence tomography (OCT) apparatus, and the like.

In an embodiment, the plurality of examination data may include theparameters described above with reference to FIG. 9 . Specifically, theplurality of examination data may include parameters such as cornealcurvature, a refractive error (myopia, astigmatism, or farsightednessdegree), a pupil size, an intraocular pressure, vision, a corneal shape,a corneal thickness, an eyeball length, a lens insertion space, an ATAdistance, an ACD-epi, an ACD-end, a CCT, a CLR, a WTW, an AL, a measureddistance between irises, and the like.

A lens size may be derived from the acquired input data using a lenssize determination model.

In addition, in an embodiment, since a degree of influence on a resultvalue varies according to types of the parameter included in the inputdata, the result value may vary for each input data.

Furthermore, in another embodiment, each input data may include at leastsome of the same parameters, but the same parameters may be derived bydifferent examination apparatuses. Even when the same parameters arederived from the different examination apparatuses, numerical valuesrepresenting the same parameters may be different. The difference mayoccur due to methods and principles of measuring parameters beingdifferent for each examination apparatus. For example, a parameter Aobtained from a UBM apparatus (wherein the parameter A represents anarbitrary parameter) is a parameter that performs the same function as aparameter A obtained from an OCT apparatus, but numerical valuesrepresenting the parameters A may be different. As described above,since accuracies of measured parameters are different according to theexamination apparatuses, input data may have different degrees ofinfluence on a result value.

In addition, each parameter or even the same parameter may have adifferent degree of influence on a result value according to whichexamination apparatus outputs a parameter. Thus, a priority betweeninput data may be changed.

In order to increase accuracy of a result value, a weight may beincreased for a parameter with a high priority or a parameter derivedfrom an examination apparatus with a high priority.

In an embodiment, the input data may include priority data. The prioritydata may be prioritized according to types of the parameters included inthe input data. For example, when the input data includes a parameterhaving a large degree of influence on deriving a lens size, the inputdata may be a first priority data. When the input data includes a secondparameter which has a smaller degree of influence on deriving a lenssize as compared with the first parameter, the input data may be asecond priority data. For convenience of description, only the firstpriority data and the second priority data have been described, but notlimited thereto. The input data may include a plurality of prioritydata.

In an embodiment, when a lens size is determined using the input dataincluding the first priority data, a first lens size may be derived, andwhen a lens size is determined using the input data including the secondpriority data, a second lens size may be derived. For example, aprobability of accuracy of a result value in the first lens size may behigher than that of the second lens size. In this case, when surgery isperformed on a person to be operated on using the first lens size, fewerside effects may occur as compared with when surgery is performed usingthe second lens size.

In an embodiment, in operation S1300 of deriving the lens size, when theinput data does not include the first priority data and does include thesecond priority data, the lens size determination unit may derive thelens size using the second priority data.

In an embodiment, the input data may necessarily include the firstpriority data. This is to ensure accuracy of a result value.

In the training operation S100, the lens size determination model maylearn the first priority data and the second priority data together orseparately. According to an embodiment, when the first priority data andthe second priority data are learned together, in operation S1300 ofderiving the lens size, any one of the first priority data and thesecond priority data may be used. According to another embodiment, whenonly the first priority data is learned, in operation S1300 of derivingthe lens size, only the first priority data may be used. When only thesecond priority data is learned, in operation S1300 of deriving the lenssize, only the second priority data may be used. Of course, not limitedthereto, and according to an embodiment, even when only the firstpriority data is learned, in operation S1300 of deriving the lens size,the second priority data may be used, and even when only the secondpriority data is learned, in operation S1300 of deriving the lens size,the first priority data may be used.

3.3. Embodiments

FIGS. 12 and 13 are diagrams illustrating the determination of a lenssize according to an embodiment. That is, in FIGS. 12 and 13 , operationS1300 described above with reference to FIG. 11 will be described inmore detail. Referring to FIGS. 12 and 13 , in an embodiment, the lenssize determination model may be implemented to include a classifier.

In an embodiment, the classifier may use a type of algorithm such as adecision tree, a support vector machine, or a random forest. This ismerely an example, and not limited thereto.

In an embodiment, the lens size determination module 1000 may input aninput data to the lens size determination model 110 and may derive alens size from the lens size determination model 110. The lens sizedetermination model 110 may include the classifier, and the classifiermay determine any one of lens sizes having preset values.

In addition, a standardized lens size may be obtained from an input dataof a person to be operated on using the lens size determination modelimplemented to include the classifier. The classifier may determine onelens size of a lens to be inserted into an eyeball of the person to beoperated on from the input data of the person to be operated on.

In addition, the standardized lens size may be an existing lens size.The existing lens size may be a size that is predetermined according toa preset standard. In an embodiment, a standardized lens size, forexample, a lens size of 12.6 mm, which is one of sizes of 12.1 mm, 12.6mm, 13.2 mm, and 13.7 mm, may be determined from the input data of theperson to be operated on by using the lens size determination modelimplemented to include the classifier.

FIGS. 14 and 15 are diagrams illustrating the determination of a lenssize according to another embodiment. That is, in FIGS. 14 and 15 ,operation S1300 described above with reference to FIG. 11 will bedescribed in more detail. Referring to FIGS. 14 and 15 , in anembodiment, the lens size determination model may be implemented toinclude a regression.

In an embodiment, the regression may use a type of algorithm such as alinear regression, a regression tree, a support vector regression, or akernel regression. This is merely an example, and not limited thereto.

In an embodiment, the lens size determination module 1000 may input aninput data to the lens size determination model 110 and may derive alens size from the lens size determination model 110. The lens sizedetermination model 110 may include the regression, and the regressionmay determine any one of lens sizes that may or may not have presetvalues.

In addition, a standardized and/or non-standardized lens size may beobtained from an input data of a person to be operated on using the lenssize determination model implemented to include the regression. Theregression may derive a probability with respect to lens sizes of a lensto be inserted into an eyeball of the person to be operated from theinput data of the person to be operated on. A lens size derived at thehighest probability may be the most suitable lens size among the lenssizes of the lens to be inserted into the eyeball of the person to beoperated on.

According to an embodiment, the output unit 1500 may output anon-standardized lens size according to a learning method of the lenssize determination model. The non-standardized lens size may beexpressed as all lens sizes including an existing lens size.Specifically, the non-standardized lens size is a size of a lens to beinserted into the eyeball in consideration of characteristics of theeyeball of the person to be operated on and may be larger or smallerthan an existing lens size or may be a size between the existing lenssizes. The non-standardized lens size may be a lens size of a lens to becustom-made in consideration of the characteristics of the eyeball ofthe person to be operated on. The non-standardized lens size may be alens size more optimized for the eyeball of the person to be operated onas compared with a standardized lens size. The non-standardized lenssize may be a lens size customized to the eyeball of the person to beoperated on.

In another embodiment, the lens size determination model 110 may beimplemented to include a classifier and a regression. That is, the lenssize determination model 110 may be implemented by combining theclassifier and the regression in series or in parallel. As an example,the lens size determination module 1000 inputs an input data into thelens size determination model 110 in which the classifier and theregression are combined and may derive lens sizes and numerical valuesfrom the combined lens size determination model 110. For example, astandardized lens size of 12.6 mm through the classifier may be derivedfrom a lens size of 12.5 mm output through the regression.Alternatively, a non-standardized lens size of 13.3 mm through theregression may be derived from a lens size of 13.2 mm output through theclassifier. This is merely an example, and the lens size output throughthe classifier and the lens size output through the regression may besimultaneously derived.

FIG. 16 is a diagram illustrating the determination of a lens sizeaccording to still another embodiment. Referring to FIG. 16 , the lenssize determination module 1000 may further include a data complementunit 1200 which supplements an input data.

When priority data may not be acquired from an examination apparatus, orin an environment in which an examination apparatus capable of acquiringpriority data is not provided, the data complement unit 1200 may derivea more accurate lens size with respect to a lens size of a lens to beinserted into an eyeball of a person to be operated on during lensimplant surgery.

According to an embodiment, the input data may include a first prioritydata and a second priority data, may include only the first prioritydata, and may include only the second priority data. When a user may useonly the second priority data and may not use the first priority data asthe input data, accuracy of a result value may be lower as compared withwhen only the first priority data is used. As in this case, even whenthe first priority data is missing, in order to improve accuracy of aresult value, the first priority data may be estimated using the datacomplement unit 1200.

The data complement unit 1200 may estimate the first priority data frominput data.

The priority data may include parameters having a large degree ofinfluence on a result value. In order to increase accuracy of a resultvalue, data with a high priority, that is, data which includesparameters having a large degree of influence on a result value, may beinput. For example, when the lens size determination unit determines alens size using a first input data including an ATA distance amongparameters, accuracy of a result value may be high. When a lens size isdetermined using a second input data including only a CCT without an ATAdistance, accuracy of a result value may be low. In this case, the firstinput data may include the first priority data having a high priority,and the accuracy of the result value may be high.

According to circumstances, even when the first priority data may not beacquired and only the second priority data may be acquired, in order toincrease accuracy, the data complement unit may estimate the firstpriority data using the second input data. The estimated first prioritydata may be used as an input data in the lens size determination unit1300. For example, an ATA distance corresponding to a missing value maybe estimated from parameters such as a CCT and vision data included inthe second input data.

In an embodiment, when an examination apparatus capable of measuring thefirst priority data is not provided, the first priority data may beestimated using the data complement unit.

In an embodiment, the data complement unit 1200 may estimate an inputdata other than the first priority data as the first priority data usinga separate formula.

In an embodiment, the data complement unit 1200 may estimate an inputdata other than the first priority data as the first priority data usinga separate formula through a data complement model. Although not shown,the data complement model may be trained based on the first prioritydata and the second priority data as learning data. As an example, thedata complement model may be trained to derive the first priority databy receiving the second priority data. When the second priority data isinput as input data, the trained data complement model may derive theestimated first priority data. This is merely an example, and notlimited thereto.

In addition, in an embodiment, the lens size determination model may usemultiple machine learning algorithms together among a plurality ofmachine learning algorithms for calculating a predicted value. As anexample, the lens size determination model may be trained using anensemble method and may estimate a lens size. Since the ensemble methodis used in the lens size determination model, accuracy of the lens sizedetermination model may be improved.

FIG. 17 is a schematic diagram illustrating a plurality of sub-models ofthe lens size determination model. Referring to FIG. 17 , the lens sizedetermination model may include the plurality of sub-models. Theplurality of sub-models may each independently determine a lens size.For example, a first sub-model may be a model that is trained using arandom forest method to determine a lens size, and a second sub-modelmay be a model that is trained using a decision tree method to determinea lens size. In addition, although only the first sub-model and thesecond sub-model are illustrated in FIG. 17 , this is merely an example,and the sub-models may be provided with a plurality of sub-models.

The plurality of sub-models may be connected in parallel. Here, inputdata inputted to the plurality of sub-models and output values outputtedfrom the plurality of sub-models may be the same or different.

The lens size determination model may output a prediction result basedon the output value of the sub-model.

The lens size determination model may include an output sub-model thatoutputs a prediction result based on the output values of the pluralityof sub-models connected in parallel. In an embodiment, when a firstoutput value and a second output value, which are output values of thefirst sub-model and the second sub-model, are the same value, the outputsub-model may output the same value. In another embodiment, when thefirst output value and the second output value, which are the outputvalues of the first sub-model and the second sub-model, are different,the output sub-model may consider the output values of the plurality ofsub-models at a certain ratio to output a prediction result or mayoutput a specific value among a plurality of output values. In otherwords, the output sub-model may give weights to the first output valuefrom the first sub-model and the second output value from the secondsub-model and may output a lens size by reflecting the weights appliedto the output values. For example, when a weight of 0.8 is given to thefirst output value, a weight of 0.2 is given to the second output value,the first output value represents 12.6 mm among standardized lens sizes,and the second output value represents 13.2 mm among the standardizedlens sizes, the output sub-model may derive a lens size of 12.6 mm,which is the first output value having a high weight, as an outputvalue.

In addition, for example, when a weight of 0.8 is given to the firstoutput value, a weight of 0.2 is given to the second output value, thefirst output value represents 12.6 mm among non-standardized lens sizes,and the second output value represents 13.2 mm among thenon-standardized lens sizes, the output sub-model may derive a lens sizeof 12.7 mm, to which a weight is reflected, as an output value.

In addition, the weight may be determined in a training process. Thatis, the training operation S100 described above with reference to FIG. 8may be performed on the lens size determination model including theplurality of sub-models, and the weight may be determined in thetraining operation S100.

In another embodiment, the output sub-model may output another valuegenerated based on the plurality of output values as a predictionresult. Here, the output value of the output sub-model may be the sametype as or different type from the plurality of output values.

In an embodiment, the first sub-model and the second sub-model may bethe same, and input data inputted to the sub-models may be different.The input data may be a first priority data or a second priority data. Ahigh weight may be given to the sub-model to which data with a highpriority is inputted. For example, a higher weight may be given to thefirst sub-model to which the first priority data is input as comparedwith the second sub-model to which the second priority data is input,thereby deriving the first output value as a lens size output value.

4. Prediction of Vaulting Value 4.1. Definition of Vaulting Value

FIG. 18 is a diagram for defining a vaulting value. The vaulting value(also referred to as a vault value) is a value representing a distancebetween a rear surface of a lens to be inserted into an eyeball of aperson to be operated on with lens implant surgery and an anteriorsurface of a crystalline lens. Specifically, the vaulting value isdefined as the shortest distance of a plurality of distances between therear surface of the lens to be inserted into the eyeball and theanterior surface of the crystalline lens. Referring to FIG. 18 , Lrepresents a lens inserted into an eyeball of a person to be operatedon, I represents an iris, C represents a crystalline lens, and Vrepresents a vaulting value. The lens L may be inserted into a spacebetween the iris I and the crystalline lens C. A plurality of distancesmay exist between the lens inserted into the eyeball and the anteriorsurface of the crystalline lens. Among the plurality of distances, theshortest distance between the rear surface of the lens and the anteriorsurface of the crystalline lens, that is, a distance between the lensand the crystalline lens in a vertical direction from a center of acornea, may correspond to the vaulting value V.

In general, a vaulting value may be measured after lens implant surgeryin order to check whether a lens having a suitable size is inserted intoan eyeball of a patient. When the vaulting value measured after thesurgery is included within a certain range, it may be determined that alens size of the lens inserted into the eyeball is a lens size suitablefor the eyeball of the patient. As an example, a vaulting value may beincluded in a certain range of 250 μm to 750 μm. In an embodiment, whenthe vaulting value is 250 μm or less, the lens inserted into the eyeballof the patient may be regarded as having a size smaller than a sizesuitable for the eyeball of the patient. When a lens having a sizesmaller than a lens size suitable for the eyeball of the patient isinserted, a cataract may be caused as described with reference to leftof FIG. 10 . In another embodiment, when the vaulting value is 750 μm ormore, the lens inserted into the eyeball of the patient may be regardedas having a size larger than a size suitable for the eyeball of thepatient. When a lens having a size larger than a lens size suitable forthe eyeball of the patient is inserted, glaucoma may be caused asdescribed with reference to right of FIG. 10 . Therefore, in order toprevent side effects of lens implant surgery, the vaulting value afterthe surgery may need to be included within an appropriate range. Thatis, there is a need to accurately design and then insert a lens beforelens implant surgery. Hereinafter, a vaulting value prediction modulefor predicting a vaulting value and a vaulting value prediction processwill be described.

4.2. Configuration of Vaulting Value Prediction Module

FIG. 19 is a diagram illustrating a configuration of the vaulting valueprediction module 2000. In an embodiment, the vaulting value predictionmodule 2000 may output a prediction vaulting value in an eyeball of aperson to be operated on from an input data.

Referring to FIG. 19 , the vaulting value prediction module 2000 mayinclude an input unit 2100, a vaulting value prediction unit 2300, andan output unit 2500.

The input unit 2100 may acquire an input data from a database. The inputdata may include a plurality of examination data of the person to beoperated on.

Specifically, the input unit 2100 may be connected directly to thedatabase to acquire the input data. In addition, the input unit 2100 mayreceive and acquire the input data from a server or other externaldevices.

The input data may include the examination data of the person to beoperated on. The examination data may be the same as that described inContent 3.1 above. Hereinafter, only different contents will bedescribed.

According to an embodiment, the input data may include an arbitraryexpected lens size of a lens to be inserted into the eyeball of theperson to be operated on. The arbitrary expected lens size may be astandardized or non-standardized lens size.

In an embodiment, the vaulting value prediction unit 2300 may predict avaulting value of the person to be operated on from the input data.

In addition, when the predicted vaulting value is included within acertain range, the vaulting value prediction unit 2300 may provide thepredicted vaulting value, and a user may determine that surgery has beenperformed using a suitable lens size based on the predicted vaultingvalue.

In an embodiment, the vaulting value prediction unit 2300 may provideinformation about whether lens implant surgery of the person to beoperated on is possible using an input lens size according to thepredicted vaulting value. For example, when the predicted vaulting valueis derived as 200 μm, which is not included within a range of 250 μm to750 μm, the vaulting value prediction unit 2300 may determine that lensimplant surgery of the person to be operated on is impossible. This ismerely an example, and not limited thereto, and to the contrary, whenthe predicted vaulting value is included within a certain range, thevaulting value prediction unit 2300 may determine that lens implantsurgery is possible.

In addition, the vaulting value prediction unit 2300 may provideinformation about whether the input lens size is suitable for lensimplant surgery of the person to be operated on according to thepredicted vaulting value. For example, when the predicted vaulting valueis 500 μm, the vaulting value prediction unit 2300 may provideinformation indicating that a lens size of 13.2 mm inputted as an inputdata is suitable for the lens implant surgery of the person to beoperated on. In addition, when the predicted vaulting value is 800 μm,the vaulting value prediction unit 2300 may provide informationindicating that a lens size of 13.2 mm inputted as an input data is notsuitable for the lens implant surgery of the person to be operated on.This is merely an example, and not limited thereto.

Specific operations of the vaulting value prediction unit 2300 will bedescribed in more detail with reference to FIG. 20 .

The output unit 2500 may output a vaulting value obtained through thevaulting value prediction unit 2300 to a user. In an embodiment, theoutput unit 2500 may provide a display that visually outputs output dataon a screen. In addition, the output unit 2500 may output various formssuch as an image and a text.

In an embodiment, the predicted vaulting value may be a criterion fordetermining a result of lens implant surgery. For example, when thepredicted vaulting value is included within a range of 250 μm to 750 μm,the user may determine that surgery has been performed using a lens sizesuitable for the eyeball of the person to be operated on.

In an embodiment, according to a result value of the vaulting valueprediction unit 2300, it is possible to output information about whetherlens implant surgery of a person to be operated on is suitable oravailable using a lens size input as input data.

4.3. Vaulting Value Prediction Process

FIG. 20 is a flowchart of a vaulting value prediction process S2000.

Referring to FIG. 20 , the vaulting value prediction process S2000includes acquiring an input data such as examination data of a person tobe operated on (S2100) and deriving a prediction vaulting value usingthe vaulting value prediction model (S2300). The vaulting valueprediction process S2000 may be performed by the vaulting valueprediction module 2000 described above with reference to FIG. 2 .

Specifically, in operation S2100 of acquiring the input data, the inputdata may include a plurality of examination data acquired from aplurality of examination apparatuses related to measurement of aneyeball of the person to be operated on. This is the same as that of thelens size determination process in Content 3.2 and thus is omitted.

In an embodiment, the input data may include the plurality ofexamination data and arbitrary lens sizes.

In operation S2300 of deriving the prediction vaulting value, a vaultingvalue corresponding to the arbitrary lens size may be predicted usingthe vaulting value prediction model based on the plurality ofexamination data of the person to be operated on.

In an embodiment, as described with reference to FIG. 3 , the vaultingvalue prediction model 120 may be trained by the training device 11 andmay derive a vaulting value predicted by the determination assistancedevice 21. In addition, the vaulting value prediction model 120 may betrained through the training operation S100 and may derive a predictedvaulting value through the determining operation S200. The mattersdescribed with reference to FIGS. 3 and 8 may be applied unchanged tothe vaulting value prediction model 120.

4.4. Embodiments

In an embodiment, the vaulting value prediction module may predict avaulting value using a plurality of examination data and/or expectedlens sizes of a person to be operated on as an input data.

FIG. 21 is a diagram illustrating the prediction of a vaulting valueaccording to an embodiment. Referring to FIG. 21 , the vaulting valueprediction unit 2300 may predict a vaulting value and a lens size usinga plurality of examination data of a person to be operated on as aninput data.

The vaulting value may be derived differently according to a size of alens to be inserted into an eyeball.

In an embodiment, when the examination data is input to the vaultingvalue prediction model 120, the vaulting value prediction module 2000may output a lens size and a predicted vaulting value together. When thelens size and the predicted vaulting value are output together as outputdata, a user may accurately determine a lens size suitable forcharacteristics of an eyeball of a person to be operated on based on thepredicted vaulting value. For example, when a lens size of 12.6 mm and apredicted vaulting value of 500 μm are output, since the predictedvaulting value is included within an appropriate range, the user maydetermine that the output lens size is a lens size suitable for thecharacteristics of the eyeball of the person to be operated on.Alternatively, when a lens size of 13.2 mm and a predicted vaultingvalue of 900 μm are output, since the predicted vaulting value is notincluded within an appropriate range, the user may determine that theoutput lens size is a lens size not suitable for the characteristics ofthe eyeball of the person to be operated on. This is merely an example,and not limited thereto.

In an embodiment, the vaulting value prediction model 120 may be trainedusing learning data for inputting examination data and outputting avaulting value and a lens size. The training operation S100 describedwith reference to FIG. 8 may be applied unchanged to a trainingoperation of the vaulting value prediction model intact.

In an embodiment, a predicted vaulting value may be a vaulting valuewithin a certain range. That is, the output unit 2500 may output avaulting value within a certain range and a lens size correspondingthereto. For example, when a vaulting value is 500 μm which is within arange of 250 μm to 750 μm, the output unit 2500 may output a lens sizeof 12.6 mm.

FIG. 22 is a diagram illustrating the prediction of a vaulting valueaccording to another embodiment. Referring to FIG. 22 , the vaultingvalue prediction unit 2300 may predict a vaulting value using aplurality of examination data and arbitrary lens sizes of a person to beoperated on as an input data.

In an embodiment, when the examination data and the arbitrary lens sizeare inputted to the vaulting value prediction model 120, the vaultingvalue prediction module 2000 may output a predicted vaulting value. Auser may determine whether the input arbitrary lens size is suitable forcharacteristics of an eyeball of the person to be operated on based onthe predicted vaulting value. For example, after the examination dataand a lens size of 13.2 mm are input as an input data, when a predictedvaulting value of 450 μm is output, it may be determined that the lenssize of 13.2 mm, which is the arbitrary lens size, is suitable for thecharacteristics of the eyeball of the person to be operated on. This ismerely an example, and not limited thereto.

In an embodiment, the vaulting value prediction model 120 may be trainedusing learning data for inputting examination data and a lens size andoutputting a vaulting value. The training operation S100 described withreference to FIG. 8 may be applied unchanged to a training operation ofthe vaulting value prediction model.

Although not shown, in an embodiment, the vaulting value prediction unit2300 may be implemented to interwork with the lens size determinationunit 1300. In this case, a user may verify accuracy of a result valuederived through the lens size determination unit 1300. For example, whena lens size of 13.2 mm derived through the lens size determination unit1300 is inputted as an input data of the vaulting value prediction unit2300 together with examination data and when a predicted vaulting valueis 500 μm, since the vaulting value is included within a certain range,it can be verified that the lens size of 13.2 mm, which is a resultvalue of the lens size determination unit, is a result value suitablefor the eyeball of the person to be operated on and accuracy of theresult value is also high.

In an embodiment, the lens size determination unit 1300 and the vaultingvalue prediction unit 2300 may be connected in series. Specifically, alens size (output data) derived using the lens size determination unit1300 may be acquired as an input data of the vaulting value predictionunit 2300. That is, a lens size, which is a result value of the lenssize determination unit, and a plurality of examination data of a personto be operated on may be inputted as the input data of the vaultingvalue prediction unit 2300. Therefore, the vaulting value predictionunit 2300 may output a prediction vaulting value corresponding to aninput lens size.

5. Determination of Lens Power 5.1. Configuration of Lens PowerDetermination Module

Even when the maximum corrected vision of a surgery eye is greater thanor equal to a target vision after lens implant surgery, quality ofvision may be degraded due to residual astigmatism after surgery. Forexample, even when, after the lens implant surgery, the corrected visionof the surgery eye reaches a target vision of 1.2 and astigmatism ispartially corrected, residual astigmatism may remain. In this case, apatient may not obtain an expected surgical result due to the residualastigmatism. Therefore, when a power of a lens used for lens implantsurgery is determined, factors of astigmatism such as cornealastigmatism may need to be considered.

In an embodiment, when a power of a lens is determined, it is necessaryto determine the power of the lens in consideration of not only themaximum corrected vision, but also corneal astigmatism caused bysurgery. In this case, the residual astigmatism is predicted in advance,and an element for correcting the residual astigmatism may be reflectedin the lens in advance. Accordingly, a user can obtain not only a targetvision but also a desired quality of vision.

FIG. 23 is a diagram illustrating a configuration of the lens powerdetermination module 3000. In an embodiment, the lens powerdetermination module 3000 may output a power of a lens to be insertedinto an eyeball of a person to be operated on from input data.

Referring to FIG. 23 , the lens power determination module 3000 mayinclude an input unit 3100, a lens power determination unit 3300, and anoutput unit 3500.

The input unit 3100 may acquire an input data from a database. The inputdata may include a plurality of examination data of the person to beoperated on.

Specifically, the input unit 3100 may be connected directly to thedatabase to acquire the input data. In addition, the input unit 3100 mayreceive and acquire the input data from a server or other externaldevices.

The input data may include the examination data of the person to beoperated on. The examination data may be the same as that described inContent 3.1 above. Hereinafter, only different contents will bedescribed.

According to an embodiment, the examination data may include measureduncorrected vision of the person to be operated, a diopter measured froman eyeball, an astigmatic axis, parameters of a cylindrical orientation,corneal astigmatism, lenticular astigmatism, data about a ratio ofmyopia and astigmatism, and the like.

According to an embodiment, the input data may include corneal incisioninformation. The corneal incision information may mean information abouta predicted or planned corneal incision in a cornea incision process ofthe person to be operated on before a lens is inserted during lensimplant surgery. In the corneal incision process of the lens implantsurgery of the person to be operated on, the corneal incisioninformation may include a corneal incision method, a corneal incisionlocation, a corneal incision direction, and/or a corneal incisiondegree. An amount of change in astigmatism may vary according to thecorneal incision location of the corneal incision information, and anastigmatism (SIA) value caused by surgery may vary according to acorneal incision size. Therefore, at the time of determining a lenspower, when the lens power is determined after astigmatism, in whichfactors such as an amount of change in astigmatism and/or astigmatismcaused by surgery are adjusted, is predicted in consideration of thecorneal incision information, it is possible to obtain an effect offurther improving quality of vision.

In an embodiment, the lens power determination unit 3300 may determine alens power suitable for the eyeball of the person to be operated on byapplying an input data such as a plurality of examination data of theperson to be operated on to the lens power determination model 130.Here, the suitable lens power may mean a lens power in which thepossibility of occurrence of side effects is minimized when lens implantsurgery is performed on the person to be operated on, and quality ofvision is high. As side effects in the determination of the lens power,there may be decreased vision and headaches according to the decreasedvision.

In an embodiment, in order to determine a suitable lens power, the lenspower determination unit 3300 inputs an input data such as a pluralityof examination data of the person to be operated on and expected cornealincision information of the person to be operated on, thereby outputtinga lens power suitable for the eyeball of the person to be operated on.

Specific operations of the lens power determination unit 3300 will bedescribed in more detail with reference to FIG. 24 .

The output unit 3500 may output information (output data) about a powerof a lens to be inserted into the eyeball of the person to be operatedon through the lens power determination unit 3300.

The output unit may output a lens power suitable for the eyeball of theperson to be operated on according to a learning method of the lenspower determination model.

According to an embodiment, when the lens power determination model isimplemented in the form of a regression, the output unit may output alens power suitable for target vision of the eyeball of the person to beoperated on. It is possible to output a lens power with the highestprobability, which is suitable for the eyeball of the person to beoperated on, among a plurality of lens powers. This is merely anexample, and not limited thereto. The lens determination model may beimplemented using a classifier. In this case, it is possible to output alens power suitable for the eyeball of the person to be operated onamong a plurality of standardized lens powers.

5.2. Lens Power Determination Process

FIG. 24 is a flowchart illustrating a lens power determination processS3000. Referring to FIG. 24 , the lens power determination process S3000may include acquiring an input data such as a plurality of examinationdata of a person to be operated on (S3100) and deriving a lens powerusing the lens power determination model (S3300). The lens powerdetermination process S3000 may be performed by the lens powerdetermination module 3000 described above with reference to FIG. 2 .

Specifically, in operation S3100 of acquiring the input data, the inputdata may include a plurality of examination data which are acquired froma plurality of examinations related to measurement of an eyeball of theperson to be operated on. In an embodiment, the plurality ofexaminations related to the measurement of the eyeball may include aslit lamp microscopic examination, a fundus examination, an automaticrefraction and corneal curvature examination, a corneal topographyexamination, and the like.

In an embodiment, the plurality of examination data may includeuncorrected vision, a location of a coma, corneal astigmatism,lenticular astigmatism, a ratio of myopia and astigmatism, and the like.

In an embodiment, the input data may include expected corneal incisioninformation of the person to be operated on. For example, the expectedcorneal incision information may include a corneal incision degree, acorneal incision location, a corneal incision direction, and the like ina corneal incision process of the person to be operated on.

In the determining of the lens power, corneal incision information maybe considered. FIG. 25 shows diagrams for describing corneal incisioninformation. Specifically, (a) of FIG. 25 is an exemplary schematicdiagram illustrating a corneal incision when astigmatism is notcorrected, and (b) of FIG. 25 is an exemplary schematic diagramillustrating a corneal incision when astigmatism is corrected.

Referring to (a) of FIG. 25 , in an embodiment, when astigmatism is notcorrected during a vision correction of a person to be operated on, acorneal incision direction may be an x-axis direction with respect to apupil. In addition, a corneal incision degree may be a quarter of alength of the pupil.

Referring to (b) of FIG. 25 , in an embodiment, when astigmatism iscorrected during a vision correction of a person to be operated on, acorneal incision direction may be a y-axis direction with respect to apupil. In addition, a corneal incision degree may be a quarter of alength of the pupil.

FIG. 25 illustrates an example that occurs in a corneal incision processof the person to be operated on, but not limited thereto, and this mayvary according to a degree of astigmatism and an astigmatism rate of theperson to be operated on.

In an embodiment, in addition to examination data, expected cornealincision information as described with reference to FIG. 25 may beinputted to be used as an input data. When the corneal incisioninformation is inputted, the lens power determination unit may determinea lens power in consideration of the corneal incision information.

5.3. Embodiments

FIG. 26 is a diagram illustrating the determination of a lens poweraccording to an embodiment. Referring to FIG. 26 , the lens powerdetermination unit 3300 may output a lens power using a plurality ofexamination data of a person to be operated on as an input data.

In an embodiment, when the examination data is inputted to the lenspower determination model 130, the lens power determination module 3000may output the lens power.

In an embodiment, the lens power determination model 130 may be trainedusing leaning data for inputting examination data and outputting a lenspower. The training operation S100 described with reference to FIG. 8may be applied unchanged to a training operation of the lens powerdetermination model.

Although not shown, in an embodiment, the lens power determination unit3300 may output a lens power and corneal incision information using theexamination data of the person to be operated on as an input data. In acorneal incision process, an expected lens power may be outputtedconcurrently with corneal incision information such as a cornealincision degree, a cornea incision location, and a corneal incisiondegree, thereby outputting a lens power in consideration of factors suchas astigmatism corrected according to the corneal incision information.

In an embodiment, the lens power determination model 130 may be trainedusing learning data for inputting examination data and outputting a lenspower and corneal incision information. The training operation S100described with reference to FIG. 8 may be applied unchanged to atraining operation of the lens power determination model.

Although not shown, in an embodiment, the lens power determination unit3300 may output a lens power, corneal incision information, andastigmatism parameters such as an SIA value caused by surgery using theexamination data of the person to be operated on as an input data. In acorneal incision process, an expected lens power may be outputtedconcurrently with corneal incision information and astigmatismparameters such as an SIA value caused by surgery, thereby outputting alens power in consideration of factors such as astigmatism correctedaccording to the corneal incision information and astigmatism caused bysurgery.

In an embodiment, the lens power determination model 130 may be trainedusing learning data for inputting examination data and outputting a lenspower, corneal incision information, and astigmatism parameters such asan SIA value caused by surgery. The training operation S100 describedwith reference to FIG. 8 may be applied unchanged to a trainingoperation of the lens power determination model.

FIG. 27 is a diagram illustrating the determination of a lens poweraccording to another embodiment. Referring to FIG. 27 , the lens powerdetermination unit 3300 may output a lens power using a plurality ofexamination data and corneal incision information of a person to beoperated on as an input data.

In an embodiment, the lens power determination model 130 may be trainedusing learning data for inputting examination data and corneal incisioninformation and outputting a lens power. The training operation S100described with reference to FIG. 8 may be applied unchanged to atraining operation of the lens power determination model.

Although not shown, in an embodiment, the lens power determination unit3300 may output astigmatism parameters such as an SIA value caused byexpected surgery using the plurality of pieces of examination data andthe corneal incision information of the person to be operated on as aninput data.

In an embodiment, the lens power determination model 130 may be trainedusing learning data for inputting examination data and corneal incisioninformation and outputting astigmatism parameters. The trainingoperation S100 described with reference to FIG. 8 may be appliedunchanged to a training operation of the lens power determination model.

Although not shown, in an embodiment, the lens power determination unit3300 may output astigmatism parameters such as an SIA value caused byexpected surgery and corneal incision information using the plurality ofexamination data and the corneal incision information of the person tobe operated on as an input data.

In an embodiment, the lens power determination model 130 may be trainedusing learning data for inputting examination data and corneal incisioninformation and outputting astigmatism parameters and corneal incisioninformation. The training operation S100 described with reference toFIG. 8 may be applied unchanged to a training operation of the lenspower determination model.

Methods according to the embodiments may be implemented in the form ofprogram instructions executable through diverse computing devices andmay be recorded in computer-readable media. The computer-readable mediamay include, independently or in combination, program instructions, datafiles, data structures, and so on. Program instructions recorded in themedia may be specially designed and configured for the embodiments ormay be generally known by those skilled in the computer software art.Computer-readable recording media may include magnetic media such ashard disks, floppy disks, and magnetic tapes, optical media such as aCD-ROM and DVD, magneto-optical media such as floptical disks, andhardware units, such as a ROM, a RAM, a flash memory, and so on, whichare intentionally formed to store and perform program instructions.Program instructions may include high-class language codes executable bycomputers using interpreters, as well as machine language codes likelymade by compilers. The hardware units may be configured to function asone or more software modules for performing the operations according tothe embodiments of the present disclosure, and vice versa.

While features and configurations of the present invention have beendescribed with reference to the embodiments thereof, the presentinvention is not limited thereto. It is apparent to those skilled in theart that various changes and modifications thereof may be made withinthe spirit and scope of the present invention, and therefore it is to beunderstood that such changes and modifications belong to the scope ofthe appended claims.

1. A method for predicting a vaulting value representing a distancebetween a rear surface of a lens to be inserted into an eyeball of aperson to be operated on with lens implant surgery and an anteriorsurface of a crystalline lens, the method comprising: inputtingexamination data of the person to be operated on and one or more lenssizes to a vaulting value prediction model; predicting vaulting valuecorresponding to the one or more input lens sizes from the vaultingvalue prediction model; and obtaining, based on the vaulting valuecorresponding to the one or more input lens sizes, a lens size for theperson to be operated on, from among the one or more lens sizes, whereinthe vaulting value prediction model is trained based on examination dataof patients who have had lens implant surgery in the past, sizeinformation of lenses inserted into eyeballs of the patients, andvaulting values measured after surgery of the patients.
 2. The method ofclaim 1, wherein the vaulting value is defined as the shortest distanceamong a plurality of distances between the rear surface of the lens tobe inserted into the eyeball of the person to be operated on with thelens implant surgery and the anterior surface of the crystalline lens.3. The method of claim 1, further comprising: providing informationabout whether a lens having a lens size corresponding to the predictedvaulting value is suitable for the eyeball of the person to be operatedon based on whether the predicted vaulting value satisfies a conditionof a predetermined range.
 4. The method of claim 3, wherein thecondition of the predetermined range satisfies the predicted vaultingvalue being included within a range of 250 to 750 μm.
 5. A method forpredicting a vaulting value representing a distance between a rearsurface of a lens to be inserted into an eyeball of a person to beoperated on with lens implant surgery and an anterior surface of acrystalline lens, the method comprising: inputting examination data ofthe person to be operated on to a vaulting value prediction model;predicting expected lens size and the vaulting value corresponding tothe expected lens size from the vaulting value prediction model; anddetermining whether the expected lens size is suitable for the eyeballof the person to be operated on based on the predicted vaulting value,wherein the vaulting value prediction model may be trained based on aplurality of examination data of patients who have had lens implantsurgery in the past, sizes of lenses inserted into eyeballs of thepatients, and vaulting values measured after surgery of the patients. 6.The method of claim 5, wherein the expected lens size is one of aplurality of preset lens sizes.
 7. The method of claim 5, wherein theexpected lens size is one of non-standardized lens sizes rather than aplurality of preset lens sizes.
 8. A device for predicting a vaultingvalue representing a distance between a rear surface of a lens to beinserted into an eyeball of a person to be operated on with lens implantsurgery and an anterior surface of a crystalline lens, the devicecomprising: a memory, which stores examination data of the person to beoperated on; and a processor, wherein the processor is configured to:input examination data of the person to be operated on and one or morelens sizes to a vaulting value prediction model, predict vaulting valuecorresponding to the one or more input lens sizes from the vaultingvalue prediction model, and obtain, based on the vaulting valuecorresponding to the one or more input lens sizes, a lens size for theperson to be operated on, from among the one or more lens sizes, whereinthe vaulting value prediction model is trained based on examination dataof patients who have had lens implant surgery in the past, sizes oflenses inserted into eyeballs of the patients, and vaulting valuesmeasured after surgery of the patients.